Classification of Pneumonia Medical Images with Convolutional Neural Networks

Ines Heidiani Ikasari; Riski Yoga  Saputra; Sendy  Prasdio; Muhammad Faisal  Kurniagis; Perani  Rosyani; Zainul  Janariandana

doi:10.55927/ijis.v4i1.13511

Authors

Ines Heidiani Ikasari universitas pamulang
Riski Yoga Saputra Universitas Pamulang
Sendy Prasdio Universitas Pamulang
Muhammad Faisal Kurniagis Universitas Pamulang
Perani Rosyani Universitas Pamulang
Zainul Janariandana Universitas Pamulang

DOI:

https://doi.org/10.55927/ijis.v4i1.13511

Keywords:

Pneumonia Classification, Convolutional Neural Networks, Chest X-Ray, Deep Learning, Medical Imaging, Diagnostic Accuracy

Abstract

Indonesia's agricultural sector faces significant challenges in maintaining rice production due to land conversion, pest attacks, and poor irrigation. Early detection of rice leaf diseases is critical to mitigating these challenges. This study applies the Random Forest (RF) algorithm to classify three rice leaf diseases: Bacterial Leaf Blight, Brown Spot, and Leaf Smut. The proposed method achieved an accuracy of 75%, demonstrating its effectiveness in disease detection. This research provides a foundation for integrating machine learning to improve crop management and agricultural productivity

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References

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